Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Pathology is the gold standard for cancer diagnosis. Numerous studies aim to automate the diagnosis based on digital slides, yet its prognostic utilities lack adequate investigation. Besides the inherent difficulties in predicting a patient’s prognosis, extracting informative features from gigapixel and heterogeneous whole slide images (WSI) remains an open challenge. We present a computational pipeline that can generate an embedded map to flexibly profile different cell populations’ local and global composition and architecture on WSIs. Our approach allows researchers to investigate tumor cells and tumor microenvironment based on these embedded maps of a reasonable size rather than dealing with gigantic WSIs. Here, we applied this pipeline to extract the texture patterns for tumor and immune cell types on the TCGA lung adenocarcinoma dataset. Based on extensive survival modeling, we have demonstrated that by pruning redundant and irrelevant features, the final prediction model has achieved an optimal C-index of 0.70 during testing. Our proof-of-concept study proves that the efficient local-global embedded maps bear valuable information with clinical correlations in lung cancer and potentially in other cancer types, warranting further investigations.

Original languageEnglish (US)
Title of host publicationComputational Mathematics Modeling in Cancer Analysis - 1st International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsWenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1-10
Number of pages10
ISBN (Print)9783031172656
DOIs
StatePublished - 2022
Event1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Virtual, Online
Duration: Sep 18 2022Sep 18 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13574 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
CityVirtual, Online
Period9/18/229/18/22

Keywords

  • Cell architecture
  • Lung adenocarcinoma
  • Nuclei classification
  • Survival analysis
  • Whole slide image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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